Flexible needle steering for computed tomography-guided interventions
Shahriari, Navid
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1
Introduction
Minimally invasive surgery (MIS) has developed significantly in the last decade due to technological advancements. It is used for different diagnos-tic and therapeudiagnos-tic procedures and it helps to lower the risks of complica-tions, shorter the hospitalization and reduce the tissue damage. Needles are commonly used in many of these procedure, such as biopsies, microwave and radio frequency ablations and brachytherapy. Accurate needle place-ment is crucial for success of such procedures, and it is very challenging in its current form. The medical imaging modalities such as computed to-mography (CT) and magnetic resonance imaging (MRI) have high spatial resolution and the lesions can be localized precisely. However, the clinicians need to align the needle with the targeted lesion manually, which requires good spatial thinking and experience. For specific procedures, such as brain surgery, it is possible to fixate a reference frame to the body, such as skull, in order to assist the clinicians to place the needle [1,2]. However, this is not applicable for all procedures. Another issue in these procedures is that the clinical needles are usually not completely rigid and have a bevel at the tip. This causes the needle to bend naturally when inserted into the body. This results in inaccurate targeting even if the needle is perfectly aligned with the lesion. In general, it gets more challenging to target lesions as those get deeper and smaller in size. Therefore, this thesis focuses on de-velopment of a robotic system and control algorithms, which can assist the clinicians perform needle placement procedure accurately.
1.1
Clinical motivation
Percutaneous needle insertion are commonly used for diagnostic procedures, such as breast, lung and liver biopsies, and therapeutic procedures, such as thermal ablation and brachytherapy [3]. Lung cancer has a high mortality rate worldwide (1.59 million deaths in 2012) [4]. Therefore, cancer-related diagnoses and therapies of the lung are amongst the important topics in the field, and early detection can increase the chance of survival [5]. In the United States and Europe lung cancer screening with low dose CT is recommended for people at high risk [6,7]. CT-guided lung biopsy is often performed for the nodules greater than 10mm, and also small fast-growing nodules. This can either be performed by core needle biopsy (CNB) or by fine needle aspiration (FNA). A core is cut through the nodule in CNB for pathological analysis, while in FNA a smaller needle is used to aspirate cell clusters of the nodule, for cytological analysis. FNA has a lower complication rate, however, CNB often results in a higher diagnostic performance [8,9]. Both CNB and FNA needles tend to deflect from their initial path because of their asymmetric tip. In the free-hand method, which is explained below, this can make the whole procedure more challenging, since it is hard to compensate for the needle deflection. However, the deflection can be used to correct the initial alignment errors by rotating the needle during the insertion of the needle. This will not only decrease the amount of required needle manipulations, but also enables targeting even small lung nodules.
In order to better express the importance of flexible needle steering, it would be beneficial to discuss the free-hand needle placement procedure. At the beginning of the procedure, the patient is placed on the table of the imaging device, and general anaesthesia is applied if required. A high contrast CT scan is performed in order to locate the lesion precisely. Based on the images the clinician decides about the entry region and subsequently the needle path to the target. The critical structures such as large blood vessels and impenetrable structures such as bones must be avoided when deciding for the path. A fiducial-grid sticker is then placed at the entry region and a new CT scan is performed. According to the fiducial-grid and the laser system of the CT scanner, the entry point is marked. Clinicians preferably try to choose the insertion point on a transverse image plane which contains the lesion in order to facilitate needle placement. At this
point, in order to reduce tissue movements and deformations, an small incision is often made before needle insertion, and if local anaesthesia is needed, it is applied at this point. The clinician then tries to align the needle or a needle guide with the target using the pre-operative CT images. The needle is usually inserted in steps and a new CT scan is performed at each step to check the needle trajectory. If the needle is not aligned with the target, the clinician may decide to manipulate the needle or to retract needle completely and re-insert it. The consequential increase of pleural punctures increases the chance of complications such as pneumothorax and pulmonary hemorrhage [10–12]. Furthermore, the nodule moves due to respiration, which can result in inaccurate needle placement. Therefore, if the patient is under local anaesthesia, breathing instructions are given to the patient prior to the procedure to minimize the lesion movement. The patient is asked to hold breath in a consistent fashion, if the nodule is close to the diaphragm [13]. When the needle is successfully placed at the lesion, the diagnostic or therapeutic procedure will start. The free-hand procedure is challenging since the clinician needs to have good spatial thinking and experience. Even then, it is difficult to compensate for the deflection of the needle inside the body. Therefore, it has been suggested to used robotic flexible needle steering, in order to assist the clinicians in such procedures.
1.2
Flexible needle steering
In this section, we discuss the different topics related to needle steering. Specifically, we elaborate on different needle designs, needle-tissue interac-tion models, steering methods and needle tracking techniques.
1.2.1 Needle design
Various flexible needle designs have been developed for steering, and those can be divided into two categories: Passive and active. Passive needles have a pre-defined shape, and steering is achieved by controlling the base motion of the needle. Needles with symmetric, beveled and pre-bend/curved tips are passive needles that have been used in many studies (Fig. 1.1) [14–16]. Active needles can change their shape, either at the tip or along the entire length. Examples of active needles are concentric tubes [17,18], pre-curved stylet [19], programmable bevel [20], tendon-actuated tip [21,22] needles
Figure 1.1: Passive needles: (a) Symmetric. (b) Beveled. (c) Pre-bend. (d) Pre-curved.
(Fig. 1.2). Passive needles need to be rotated along their longitudinal axis in order to control their path through the soft tissue. The rotation of the needle may cause tissue damage [23]. On the other hand, active needles can be steered in any direction without rotating the needle along its longitudinal axis. In order to model the interaction of the needles with a tissue, several models have been developed which are discussed below.
1.2.2 Needle-tissue interaction modelling
In this section, we discuss three different methods used to model the needle-tissue interaction. The interaction depends on the deformation of both the needle shaft and surrounding tissue. Here we focus only on flexible needles with a bevel at the tip.
Nonholonomic kinematics
The nonholonomic model describes the motion of the needle using a bicycle model with a fixed front wheel angle [15]. Two hypothetical wheels are placed at the needle tip as depicted in Fig. 1.3. The angle of the front wheel (φ) cause the bicycle to follow a circular path with a constant radius, which is called radius of curvature (κ). The direction of the trajectory can be controlled by rotating the needle along its shaft. It is demonstarted in [15] that the pose of the needle’s tip can be calculated using:
˙ x ˙ y ˙ z ˙ α ˙ β ˙γ = sin(β) 0 −cos(β)sin(α) 0 cos(α)cos(β) 0 κ cos(γ)sec(β) 0 κ sin(γ) 0 −κ cos(γ)tan(β) 1 u1 u2 , (1.1)
Figure 1.2: Active needles: (a) Concentric tubes (Webster III et al.). (b) Pre-curved stylet (Okazawa et al.). (c) Programmable bevel (Ko et al.). (d) Tendon-actuated (Roesthuis et al.).
where x, y and z are the tip position in 3D space, and α, β and γ are the yaw, pitch and roll, respectively. The control inputs are denoted by u1 and
u2. The distance between back wheel, front wheel and the needle tip (a and
b) and the angle (φ) should be calibrated prior to the experiments. These three parameters depend on the needle, tissue and the insertion velocity.
Finite elements models
Finite elements method (FEM) has been used to simulate both the needle and the surrounding tissue [24]. The needle can be simulated using a FEM model which takes into account the geometric nonlinearities [25]. The tissue can be modelled as a mesh of 2D or 3D polyhedral elements, which can deform when the needle advances into it. The FEM model can be used to estimate the needle-tissue contact forces resulting from tissue deformation. Furthermore, It can be used to study the effect of external forces on the tissue. This can be used to push the tissue (and therefore the target) in a certain direction, in order to reduce targeting error. Despite all the benefits, the FEM model is computationally expensive and it is not suitable for real-time application. The performance can be improved by decreasing the accuracy of the model.
Figure 1.3: Needle-tissue interaction: (a) Nonholonomic model: Two hy-pothetical wheels are placed at the needle tip. The front wheel is at a fixed angle (φ) with respect to the beck wheel. The distance between the back wheel and the needle tip is a and from the tip to the front wheel is b. Two control inputs (u1 and u2) are used to move the needle on a planned
tra-jectory. (b) Mechanics-based model: The forces (fi) acting on the needle
shaft are shown. K is the tissue stiffness and F is the force applied to the tip. The dashed-line shows the initial needle path, and the solid line depicts the current needle pose.
Mechanics-based models
Mechanics-based models are adapted from beam theories [25–27]. In these models, the fact that the needle deflection and tissue deformation are cou-pled is considered. The Euler-Bernoulli equation is used to relate the needle’s deflection (v) to the applied load (fi) (Fig. 1.3):
d2 dx2 EId 2v dx2 = fi, (1.2)
where x is the position and EI is the flexural rigidity of the needle. Inte-grating twice both sides of eq. (1.2) with respect to position (x), will result in the deflection (v). The tip force (F ) should also be considered in the integration.
Glozman et al. used the formulation mentioned above to approximated the shape of the needle with several third order polynomials [28]. The coeffi-cients of the polynomials are found using the model. Misra et al. developed an analytical model for the loads at the tip, based on the geometry of the needle and material properties of the tissue [29]. They used microscopic ob-servations to derive a model that calculates the deflection of a bevel-tipped needle. Mechanical properties of the tissue and the needle are needed for
mechanics-based models. These properties can be measured pre-operatively for homogeneous phantoms, however, for heterogeneous phantoms this can only be roughly estimated. The models which are discussed here can be used to control the needle trajectory. Several needle steering algorithms have been discussed in literature which are presented below.
1.2.3 Steering algorithms
In this section, we focus on needle steering algorithms which are specifi-cally designed for passive needles. These algorithms use the needle-tissue interaction models discussed in section 1.2.2, in order to control the tra-jectory of the needle. The three major methods discussed in literature are presented below.
Tip-steering
Tip-steering is the most widely used method for needle steering. It can be applied to bevel-tipped, pre-bend and pre-curved needles. These needles deflect naturally while inserted into the body due to the forces exerted to the asymmetric tip. Tip-steering uses this natural deflection to control the trajectory of the needle [16]. It is important to notice that the amount of needle deflection depends on several parameters, such as tissue stiffness, diameter and material of the needle, bevel angle and etc.. In tip-steering algorithms, the focus is not on altering the amount of deflection, but to control the trajectory.
Duty-cycling
In contrast with tip steering, in duty-cycling method, the amount of de-flection is controlled through periodic needle rotations [30]. If the needle is rotated constantly, the trajectory will be a straight line, and if the rotation period goes to zero, the needle bends with the maximum deflection. The fact that one can alter the amount of deflection is useful for controlling the needle trajectory, however, this methods usually applies many needles ro-tations which cause a lot of tissue damage. Furthermore, it can be applied only to bevel-tipped needles.
Base-manipulation
The base-manipulation algorithm is originally developed for symmetric nee-dles. It uses the mechanical model of needle-tissue interaction to solve for forward and backward kinematics. In this method, transverse motion of the base of the needle (which is out of the body) is used to deform the tissues and bend the needle [28]. Base-manipulation provides a good steerability at low insertion depth. However, as the needle goes deeper, the steerability decreases and the manipulation can result in large tissue stress and possible tissue damage.
1.2.4 Tracking
The needle shape, or at least the needle’s tip needs to be tracked in order to perform any type of needle steering accurately. The real-time tracking information can be used to close the control loop. The needle can be tracked using either different imaging modalities or sensors. Below we discuss the various tracking methods used in literature.
Image-based tracking
Researchers have used ultrasound [31], magnetic resonance imaging (MRI) [32] and computed tomography (CT) [33] images for needle placement pro-cedures. Ultrasound has a high frame rate with respect to MRI and CT and it is more suitable for real-time application. However, the image quality is also lower. As a result, there has been an extensive research on needle segmentation in ultrasound images.
The ultrasound is usually used in three different modalities: 1. 2D sagit-tal. 2. 2D transverse. 3. 3D volumetric. 2D sagitall was one of the first modalities that was used for needle steering. The ultrasound probe is placed parallel to the needle shaft, and the steering is often performed in 2D space [34]. If the needle moves out of the ultrasound image plane, it cannot be tracked any further. The 2D transverse images show a cross sectional view of the needle. The probe is places perpendicular to the needle shaft in this case. The transverse images are usually used to track the needle tip in 3D space. In order to achieve this, the probe is translated along the needle shaft using motors, while the needle is inserted [35]. However, it is challenging to keep the probe exactly at the tip of the needle, in order to
avoid imaging the shaft instead of the tip. The 3D volumetric imaging can be used to address this issue. It provides a 3D volume in which the tip of the needle can be tracked accurately [36]. However, 3D ultrasound imaging usually have a low frame rate and it is not suitable for real-time applica-tions. Furthermore, ultrasound images are noisy and often contain artifact specially in biological tissue. Therefore, different filtering techniques (such as Kalman filters) have been used to track the needle accurately. The as-sumption in filter design is that the motion of the needle tip is slow in imaging plane. For instance, linear Kalman filters are designed to reduce the fast and large changes in the estimated needle tip position [37,38].
Sensor-based tracking
As discussed above, medical imaging modalities have certain limitations for needle placement applications. One way to over come these limitations is to use other sensors along with the imaging device or as a standalone system. There are two sensors which are used for needle steering, fiber Bragg grating (FBG) and electromagnetic (EM) sensors. FBG sensors use the frequency change in light beams in order to measure the mechanichal strain [39]. Roesthuis et al. used an array of FBG sensors to reconstruct the the shape of the entire needle in 3D space [40]. FBG measurements have less noise with respect to imaging and it has a much higher refresh rate (up to 20KHz). However, the fibers are fragile and this technologies is still in research and it is expensive for clinical use. EM sensors have also been used in several studies with different tracking systems, which are commercially available. One of the widely used systems is NDI Aurora (Northern Digital Inc., Waterloo, Canada). The smallest sensors for this setup is a 5-DOF cylindrical sensor with a diameter of 0.5mm and height of 8.0mm. Aurora can measure multiple sensors at the same time with a refresh rate of maximum 30Hz and the cost of the system is the relatively low. However, it is sensitive to neighbouring metallic objects and other fields, and it can affect the measurements accuracy.
It is important to mention that, one imaging modality is always required to locate the lession in the body and to register the other tracking systems with respect to body. In case of imaging systems with ionizing radiations, such as CT scanner, it is beneficial for the clinicians and the patients to reduce the number of scans as much as possible. Therefore, using
sensor-based tracking methods are of great importance.
1.3
Computed tomography-compatible setups for
needle steering
Various devices have been developed over the past two decades for posi-tioning and steering needles. In this section we focus on the systems which were specifically designed for thorax and abdomen. These devices could be categorized as passive and active. Below we will discuss each category and elaborate more on it.
1.3.1 Passive devices
Passive devices are setups which assist the clinicians to align the needle towards the target without any physical interactions. There are three main types passive devices: 1. Tracking systems, 2. Gravity referenced, and 3. Laser projection.
Medical imaging modalities, such as CT and MRI, which are commonly used for needle placement procedures are not time. Therefore, real-time systems such as optical tracking and electromagnetic (EM) tracking devices could be beneficial. Examples of such systems are Stryker (Kala-mazoo, USA) optical navigation system (Fig. 1.4,a)and NDI Aurora EM tracker. In both cases, one set of sensor/marker is attached to the patient as the ground truth, and another set of sensor/marker is attached to a needle holder. The system calculates the relative pose of the needle with respect to the planed insertion pose, and give a feedback to the clinician. Both systems have high accuracy, however, optical trackers need an unob-structed line of sight, and EM trackers are sensitive to neighboring metallic object.
Gravity referenced devices use the gravity force as a reference vector. For instance, a two-dimensional bubble level is used in the system shown in Fig. 1.4,b [41]. The bubble level is used to hold the device parallel to the ground, and the needle is then aligned with the target using a protractor. AccuPlace (Inrad Inc., Kentwood, USA) is a disposable and commercially available device which uses the same concept (Fig. 1.4,c) [42].
Laser projection systems use laser beams to visualize the planned needle trajectory. The projection is used as the reference for the clinician in order
to position the needle. Fig . 1.4 shows two examples of such systems. Unger et al. developed the system shown in Fig 1.4,d [43]. The system has several motion stages which enable the user to move the laser module manually, in order to position it according to a pre-operative path planner. Another example is SimpleCT (NeoRad AS, Oslo, Norway) shown in Fig. 1.4,e), which is similar to the work of Unger, but streamlined [44].
1.3.2 Active devices
Active devices are setups which provide physical guidance for placing the needle. Active devices can be categorized as patient-mount and non-patient-mount which are discussed below.
Patient-mount
Patient-mount systems are directly attached to the body of the patient. Therefore, it should be small and light weight. One advantage of such sys-tem is that in case of body movement, it also moves with the body. This results in minimizing the targeting error due to body motions. The assump-tion here is that the target moves similar to the skin. Another advantage of such system is that the whole system is scanned with the patient and conse-quently it can act as a reference frame for targeting. Several patient-mount systems have been developed and we discuss some on them below.
Simplify systems (NeoRad AS, Oslo, Norway) depicted in Fig. 1.5,a has one rotating arc [45]. The needle guide is attached to the arc and can move along the arc. The clinician can use the two degrees-of-freedom to align the needle with the target. Similar idea was used in SeeStar (AprioMed AB, Uppsala, Sweden), where two perpendicular rotating arc are used to move the needle guide [46]. The advantage of Simplify system over SeeStar is that the system can be detached from the patient after needle insertion, without retracting the needle. While for SeeStar system the needle should be retracted before one can remove the system.
Robopsy system is another device designed for CT-guided percouta-neous biopsies and the mechanical design concept is similar to SeeStar [47]. The positioning of the needle guide is manual in Simplify and SeeStar. In contrast, Robopsy uses two stepper motors to move the needle guide. In addition, there are two stepper motors to clamp/release the needle and also insert it automatically. Tha motors are placed such that the stay out of
transverse plans to avoid image distortions. Demathelin et al. also devel-oped a CT-compatible 5-DOF system, named CT-Bot, which is actuated by ultrasonic peizo motors [48]. Three DOFs are used to position a needle driver, which has two DOFs for steering the needle. The fiducials on the robot are used to register it in the CT scanner reference frame.
Non-patient-mount
These systems are not mounted on the patient and can be table-, floor-and gantry-mounted. The table-mounted systems have the benefit that they enter the scanner with the patient, similar to patient-mount systems. Floor- and gantry-mounted systems are fixed with respect to the scanner, and do not enter into the scanner.
The system shown in Fig. 1.6,a is developed by Siemens (Munich, Germany). It has a 2-DOF parallelogram structure with a remote-center-of-motion (RCM). The RCM point is the insertion point, and it is placed at the proper position using an arm. The system is not actuated and the clinician should place it according to the CT images. Stoianovici et al. developed a system with 5-DOF, which is mounted on the table and goes over the patient using a bridge structure (Fig. 1.6,b) [49]. It consists of a 3-DOF XYZ linear stage, and a 2-DOF RCM needle guide. The system is not actuated and the clinician should used the linear stages to position the needle at the insertion point, and then align the needle with the target using the RCM mechanism.
Zhou et al. used a Mitsubishi RV-E2 6-DOF robotic arm to control the position of the needle [33]. The system is mounted on the floor, and a long end-effector is attached to the robot (Fig. 1.6,c). The end-effector has a needle gripper and it can enter the scanner bore. A vision system is also used to track the chest motion and compensate for it using the robotic arm. Tovar-Arriaga et al. also used a robotic arm (DLR/KUKA Light Weight Robot III) to position a needle holder. An optical system is used to register the robot with respect to the scanner. The system place the needle holder at the insertion point automatically, using the planning information.
Figure 1.4: Passive needle positioning devices: (a) Stryker (Kalamazoo, USA), optical navigation system. (b) Palestrant I (Palestrant et al.), grav-ity referenced system. (c) AccuPlace (Inrad Inc., Kentwood, USA), gravgrav-ity referenced system. (d) Unger et al., laser projection. (e) SimpliCT (Neo-Rad AS, Oslo, Norway), laser projection.
Figure 1.5: Patient mount systems: (a) Simplify, NeoRad AS (Oslo, Nor-way) (b) SeeStar, AprioMed AB (Uppsala, Sweden) (c) Robopsy, Gupta et al. (d) CT-Bot, Demathelin et al.
Figure 1.6: Non-patient mount systems: (a) Siemens (Munich, Ger-many). (b) AcuBot, Stoianovici et al. (c) Mitsubishi RV-E2, Zhou et al.. (d) DLR/KUKA Light Weight Robot III, Tovar-Arriaga et al.
1.4
Contributions and outline of the thesis
The significant contributions of this thesis are as follows. First, an ac-tuated 4-DoF CT-compatible remote-center-of-motion needle insertion de-vice is designed and evaluated. The system design, choice of materials and form factors are according to CT-guided interventions. This device is used through out this thesis in order to study and address different challenges within needle steering domain. A multi-sensor data fusion scheme using unscented Kalman filter is developed in order to fuse FBG data with US images and also intermittent CT images with real-time EM tracking data. The data fusion is crucial, because high targeting accuracy depends on accu-rate estimation of the needle pose. Next, a new image processing algorithm for needle tracking in biological tissue based on Fourier descriptors is devel-oped and tested using US images. A motion compensation algorithm based on force measurements and EM tracking data is proposed to compensate the physiological motion during an intervention. Finally, a new hybrid con-trol algorithm for flexible needle steering and a pre-operative path planner are developed. The hybrid control algorithm combines base-manipulation and tip-steering methods. This is used in experiments in gelatin, biological tissue and human cadaver using clinical fine-needle-aspiration needles to evaluate the performance.
The next five chapters of the thesis are published (or under review) archival journal or peer-reviewed conference papers of the author. The last chapter concludes this work and provide guideline for future work. The thesis is outlined as follows:
Chapter 2 present the design and development of a CT-compatible needle insertion device (NID). The NID has two degrees-of-freedom which are used to insert and rotate the needle. The setup is tested in the CT scanner for compatibility and noise measurements are performed. Several needle steering experiments in gelatin and biological tissue has been per-formed using EM tracker and CT images.
Considering the results in the previous chapter, in Chapter 3 a data fusion algorithm based on unscented Kalman filter is developed. Ultra-sound images are fused with FBG sensor data, and an actuated-tip needle is employed. The fused measurement data are used to closed the control loop.
degrees-of-freedom remote-center-of-motion arm is design through a paral-lel mechanism. The arm is used to rotate the NID at the insertion point. The arm is mainly made of plastic and carbon fibre rods, which are CT-compatible. All the metallic parts and motors are design to stay out of the field of CT scanner to minimize the interference.Real-time feedback of needle pose is important in order to achieve high accuracy. CT images are also required to find the location of the targeted lesion with respect to the needle’s tip. Therefore, in this chapter, the data fusion scheme discussed in Chapter 3 is modified to fuse real-time EM tracking data with intermittent CT image. In order to make the experiments more realistic, needle steering experiments are performed in an anthropomorphic phantom of the chest.
During lung and liver procedures, physiological motions such as breath-ing and beatbreath-ing heart cause the body and the targeted lesion to move. In Chapter 5, a control scheme is discussed in order to compensate for physio-logical motions. A robot arm is used to move a phantom with a trajectory similar to the motion liver during breathing. The NID, which is discussed in Chapter 2, is attached to another robot arm through a force sensor. An EM tracker is used to track the pose of needle’s tip in 3D space. The target motion is tracked using an ultrasound probe. The control algorithm uses the force measurements to compensate for the phantom motion, and it uses the EM tracking data and ultrasound images to steer the needle towards the target. The proposed algorithm is tested both in gelatin phantom and bovine liver.
Chapter 6 presents human cadaver studies using a new hybrid control al-gorithm which combines base-manipulation and tip-steering methods. The setup discussed in Chapter 3 is used in order to apply the hybrid control. A poperative path planner is developed which considers the clinical re-quirements. Several needle steering experiment are performed in the lungs of a human cadaver. In order to keep the experiments as realistic as possi-ble, the work-flow is kept similar to the current clinical practice and clinical fine-needle-aspiration needles are used. Finally Chapter 7 concludes this work and provide recommendations for future work.
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